Its suite of specialized deep learning and machine learning libraries includes tools like scikit-learn, Keras and TensorFlow, which enable data scientists to develop sophisticated data models that plug directly into a production system. Plus, Python is particularly well suited for deploying machine learning at a large scale. Matplotlib for building data visualizations.Pandas for data manipulation and analysis.

Numpy for handling large dimensional arrays.

Several Python libraries support data science tasks, including the following: In fact, Python is one of the most popular programming languages in the world, just behind Java and C. Released in 1989, Python is easy to learn and a favorite of programmers and developers. Python is a general-purpose, object-oriented programming language that emphasizes code readability through its generous use of white space. Increasingly, the question isn’t which to choose, but how to make the best use of both programming languages for your specific use cases. The main difference is that Python is a general-purpose programming language, while R has its roots in statistical analysis.

Free to download for everyone, both languages are well suited for data science tasks - from data manipulation and automation to business analysis and big data exploration. In many ways, the two open source languages are very similar.

Although both languages are bringing the future to life - through artificial intelligence, machine learning and data-driven innovation - there are strengths and weaknesses that come into play. If you work in data science or analytics, you’re probably well aware of the Python vs. Explore the basics of these two open-source programming languages, the key differences that set them apart and how to choose the right one for your situation.

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